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The advancement of biomedical research heavily relies on access to large amounts of medical data. In the case of histopathology, Whole Slide Images (WSI) and clinicopathological information are valuable for developing Artificial…
In this paper, we present BIMS (Biomedical Information Management System). BIMS is a software architecture designed to provide a flexible computational framework to manage the information needs of a wide range of biomedical research…
Computational Pathology (CPath) is an emerging field concerned with the study of tissue pathology via computational algorithms for the processing and analysis of digitized high-resolution images of tissue slides. Recent deep learning based…
Summary: Data management in clinical metabolomics studies is often inadequate. To improve this situation we created LabPipe to provide a guided, customisable approach to study-specific sample collection. It is driven through a local client…
The healthcare sector is an important pillar of every community, numerous research studies have been carried out in this context to optimize medical processes and improve care quality and facilitate patient management. In this article we…
Multiple instance learning (MIL) is a promising approach for weakly supervised classification in pathology using whole slide images (WSIs). However, conventional MIL methods such as Attention-Based Deep Multiple Instance Learning (ABMIL)…
Recent advances in agentic artificial intelligence, i.e. systems capable of autonomous perception, reasoning, and tool use, offer new opportunities for digital pathology. In this pilot study, we evaluate whether two agentic multimodal AI…
The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, which is detrimental to the reproducibility of scientific…
Anomaly detection is an emerging approach in digital pathology for its ability to efficiently and effectively utilize data for disease diagnosis. While supervised learning approaches deliver high accuracy, they rely on extensively annotated…
Advances in foundation modeling have reshaped computational pathology. However, the increasing number of available models and lack of standardized benchmarks make it increasingly complex to assess their strengths, limitations, and potential…
The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data…
Quality assurance is a critical but underexplored area in digital pathology, where even minor artifacts can have significant effects. Artifacts have been shown to negatively impact the performance of AI diagnostic models. In current…
Developing generalizable AI for medical imaging requires both access to large, multi-center datasets and standardized, reproducible tooling within research environments. However, leveraging real-world imaging data in clinical research…
Computational pathology holds substantial promise for improving diagnosis and guiding treatment decisions. Recent pathology foundation models enable the extraction of rich patch-level representations from large-scale whole-slide images…
Pathology underpins modern diagnosis and cancer care, yet its most valuable asset, the accumulated experience encoded in millions of narrative reports, remains largely inaccessible. Although institutions are rapidly digitizing pathology…
Computational pathology, integrating computational methods and digital imaging, has shown to be effective in advancing disease diagnosis and prognosis. In recent years, the development of machine learning and deep learning has greatly…
The connection between the design and delivery of health care services using information technology is known as health informatics. It involves data usage, validation, and transfer of an integrated medical analysis using neural networks of…
Many deep learning based automated medical image segmentation systems, in reality, face difficulties in deployment due to the cost of massive data annotation and high latency in model iteration. We propose a dynamic interactive learning…
The rapid digitization of histopathology slides has opened up new possibilities for computational tools in clinical and research workflows. Among these, content-based slide retrieval stands out, enabling pathologists to identify…
Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are…